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Record W4414851645 · doi:10.1097/gox.0000000000007186

Cost-Utility Analysis of Ligament Reconstruction Tendon Interposition Versus Suture Suspension Arthroplasty for Thumb Osteoarthritis

2025· article· en· W4414851645 on OpenAlexaff
Chloe R. Wong, Alice Zhu, David R. Urbach, Helene Retrouvey, Christopher D. Witiw, Heather L. Baltzer

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsMcMaster University Medical CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsOsteoarthritisThumbArthroplastyFibrous jointLigament

Abstract

fetched live from OpenAlex

Background: Thumb carpometacarpal joint osteoarthritis (CMCJ OA) is a common degenerative condition that causes pain, stiffness, and disability, reducing quality of life. Surgery is a well-established treatment option when conservative management fails, but the optimal surgical approach remains debated. This study compared the cost-utility of trapeziectomy with ligament reconstruction and tendon interposition (LRTI + T) versus suture suspension arthroplasty (SSA) for CMCJ OA. Methods: A Markov microsimulation model was developed to compare LRTI + T and SSA from a hospital payer perspective. Outcomes included incremental cost-effectiveness ratio, quality-adjusted life years (QALYs), total cost, and net monetary benefit. Clinical outcomes such as complication rates and revision surgery were also evaluated. Results: LRTI + T had a higher complication rate (14.6%) than SSA (9.8%), but SSA had a slightly higher revision rate (7.1% versus 5.7%). Over a lifetime, SSA provided an incremental gain of 0.25 QALYs but was marginally more expensive ($2855 versus $2842). SSA yielded an incremental cost-effectiveness ratio of $53.80 per QALY, making it the more cost-effective strategy. Conclusions: SSA is a cost-effective alternative to LRTI + T, offering valuable insights for clinicians and policymakers optimizing care for CMCJ OA patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.320
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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